166 citations · 595 across the 71 of their papers we have counts for
11 papers · 1 filter
Batch Predictive Inference
Yonghoon Lee, Eric Tchetgen Tchetgen, Edgar Dobriban
Constructing prediction sets with coverage guarantees for unobserved outcomes is a core problem in modern statistics. Methods for predictive inference have been developed for a wid…
A Confidence Interval for the Expected Calibration Error
Yan Sun, Pratik Chaudhari, Ian J. Barnett +1
Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains…
Evaluating the Performance of Large Language Models via Debates
Behrad Moniri, Hamed Hassani, Edgar Dobriban
Large Language Models (LLMs) are rapidly evolving and impacting various fields, necessitating the development of effective methods to evaluate and compare their performance. Most c…
Watermarking Language Models with Error Correcting Codes
Patrick Chao, Yan Sun, Edgar Dobriban +1
Recent progress in large language models enables the creation of realistic machine-generated content. Watermarking is a promising approach to distinguish machine-generated text fro…
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
Xinmeng Huang, Shuo Li, Edgar Dobriban +3
The growing safety concerns surrounding large language models raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safe…
Uncertainty in Language Models: Assessment through Rank-Calibration
Xinmeng Huang, Shuo Li, Mengxin Yu +5
Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctl…
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Patrick Chao, Edoardo Debenedetti, Alexander Robey +9
Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, w…
Inference in Randomized Least Squares and PCA via Normality of Quadratic Forms
Leda Wang, Zhixiang Zhang, Edgar Dobriban
Randomized algorithms can be used to speed up the analysis of large datasets. In this paper, we develop a unified methodology for statistical inference via randomized sketching or…
Minimax Optimal Fair Classification with Bounded Demographic Disparity
Xianli Zeng, Guang Cheng, Edgar Dobriban
Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a \…
Conditional Predictive Inference for Missing Outcomes
Yonghoon Lee, Edgar Dobriban, Eric Tchetgen Tchetgen
We study the problem of conditional predictive inference on multiple outcomes missing at random (MAR) -- or equivalently, under covariate shift. While the weighted conformal predic…
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
Xianli Zeng, Kevin Jiang, Guang Cheng +1
Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classifica…